Separate biofuel supply; add biofuel boiler

CHANGELOG.md

@@ -4,11 +4,13 @@
 
 ### Added (models)
 
+* **ADD** Specific biofuel energy carrier in the Calliope model, which can be used to generate electricity or to meet heat demand (#417).
+
 * **ADD** Industry module: iron and steel, "default" combined categories. NOT CONNECTED TO THE MAIN WORKFLOW. (Fixes #308, #309, #310, #347, #345 and #346)
 
 * **ADD** Spatial resolution that aligns with the regions defined by the [e-Highway 2050 project](https://cordis.europa.eu/project/id/308908/reporting) (`ehighways`) (#370).
 
-* **ADD** Heat demand (#284, #343, #389, #275) and heat pumps with variable COP to meet that demand (#80, #39).
+* **ADD** Heat demand (#284, #343, #389, #275), and heat pumps with variable COP and biofuel boilers to meet that demand (#80, #39).
 
 * **ADD** fully-electrified road transportation (#270, #271, #358). A parameter allows to define the share of uncontrolled (timeseries) vs controlled charging (optimised) by the solver (#338). Data for controlled charging constraints is readily available (#356), but corresponding constraints are not yet implemented (#385).
 

Snakefile

@@ -104,6 +104,7 @@ rule module_with_location_specific_data:
         capacity_factors = config["capacity-factors"]["average"],
         max_power_densities = config["parameters"]["maximum-installable-power-density"],
         heat_pump_shares = config["parameters"]["heat-pump"]["heat-pump-shares"],
+        biofuel_efficiency = config["parameters"]["biofuel-efficiency"],
     wildcard_constraints:
         # Exclude all outputs that have their own `techs_and_locations_template` implementation
         group_and_tech = "(?!transmission\/|supply\/biofuel).*"
@@ -162,10 +163,11 @@ rule model:
                 "techs/supply/rooftop-solar.yaml",
                 "techs/supply/wind-offshore.yaml",
                 "techs/supply/nuclear.yaml",
+                "techs/conversion/electricity-from-biofuel.yaml"
             ]
         ),
-        heat_supply_timeseries_data = (
-            "build/models/{resolution}/timeseries/supply/heat-pump-cop.csv",
+        heat_timeseries_data = (
+            "build/models/{resolution}/timeseries/conversion/heat-pump-cop.csv",
             "build/models/{resolution}/timeseries/supply/historic-electrified-heat.csv",
         ),
         capacityfactor_timeseries_data = expand(
@@ -198,7 +200,8 @@ rule model:
                 "techs/demand/heat.yaml",
                 "techs/demand/electrified-heat.yaml",
                 "techs/storage/heat.yaml",
-                "techs/supply/heat-from-electricity.yaml",
+                "techs/conversion/heat-from-electricity.yaml",
+                "techs/conversion/heat-from-biofuel.yaml",
                 "techs/supply/historic-electrified-heat.yaml"
             ]
         )
@@ -258,10 +261,10 @@ rule test:
         electrified_heat_demand = "build/models/{resolution}/timeseries/demand/electrified-heat.csv",
         heat_demand = "build/models/{resolution}/timeseries/demand/heat.csv",
         historic_electrified_heat = "build/models/{resolution}/timeseries/supply/historic-electrified-heat.csv",
-        cop = "build/models/{resolution}/timeseries/supply/heat-pump-cop.csv"
+        cop = "build/models/{resolution}/timeseries/conversion/heat-pump-cop.csv"
     params:
         config = config,
-        test_args = ["--pdb"]  # add e.g. "--pdb" to enter ipdb on test failure
+        test_args = []  # add e.g. "--pdb" to enter ipdb on test failure
     log: "build/logs/{resolution}/test-report.html"
     output: "build/logs/{resolution}/test.success"
     conda: "./envs/test.yaml"

docs/model/customisation.md

@@ -15,13 +15,13 @@ You have the following three options:
 With the Calliope model in your hands, you will be able to change any model parameter, any technology specifics, and the model definition to your liking.
 This kind of customisation can be useful to get to know the model and its parameters.
 To create reliable results, we advise making manual changes only to the model definition (`example-model.yaml`) as this makes it possible to trace those changes later.
-A typical customisation here would be to change the solver from `gurobi` to an open-source solver, e.g. `cbc` (see [Calliope's documentation](https://calliope.readthedocs.io/en/v{{ calliope_version }}/user/config_defaults.html#run-configuration)).
+A typical customisation here would be to change the solver from `gurobi` to an open-source solver, e.g. `cbc` (see [Calliope's documentation](<https://calliope.readthedocs.io/en/v{{> calliope_version }}/user/config_defaults.html#run-configuration)).
 We consider all Euro-Calliope model subcomponents (everything other than the model definition itself) as a toolbox from which you can choose to define your model -- see the [Import customisation option](./customisation.md#importing-modules).
 
 ## Importing modules
 
 The `example-model.yaml` definition file in each resolution sub-directory (e.g. `national/example-model.yaml`) specifies a list of other files to bring together to describe the model (under the `import` key).
-This list can be changed by the modeller to select a combination of different files (see also [Calliope's documentation](https://calliope.readthedocs.io/en/v{{ calliope_version }}/user/building.html#files-that-define-a-model)).
+This list can be changed by the modeller to select a combination of different files (see also [Calliope's documentation](<https://calliope.readthedocs.io/en/v{{> calliope_version }}/user/building.html#files-that-define-a-model)).
 These files represent "modules" of the model definition and contain everything necessary for a given technology or technology group to exist.
 For instance, `techs/supply/hydro.yaml` defines two technologies (under the `techs` key) which will convert river flows into electricity.
 It also places that technology in every relevant modelled location (under the `locations` key), along with any location-specific information that is needed; in this case, the maximum capacity of hydropower in that location.
@@ -86,6 +86,8 @@ Here, we describe each module in terms of the technologies they contain (`callio
 
         **electric_heater_heat_storage_small**: Storage buffer for direct electric heaters which inherits from the `heat_storage_small` abstract technology group, assuming a domestic (small scale) application.
 
+        **biofuel_heat_storage_small**: Storage buffer for biofuel boilers which inherits from the `heat_storage_small` abstract technology group, assuming a domestic (small scale) application.
+
 ??? note "storage/hydro.yaml"
 
     === "Technologies"
@@ -102,9 +104,27 @@ Here, we describe each module in terms of the technologies they contain (`callio
 
     === "Technologies"
 
-        **biofuel**: Biofuel
+        **biofuel**: Biofuel supply, limited per model location to a total annual production.
+
+    === "Overrides"
+
+        **biofuel_flow_limits**: Distribute annual total production limit evenly across all hours of the year and allow biofuel storage up to 50% of total annual production.
+
+??? note "conversion/heat-from-biofuel.yaml"
+
+    === "Technologies"
+
+        **biofuel_boiler**: Biofuel-consuming boiler.
+
+        **biofuel_tech_heat_to_demand**: Dummy technology to convert biofuel boiler output to a carrier that can be used to meet heat demand.
 
-??? note "supply/heat-from-electricity.yaml"
+??? note "conversion/electricity-from-biofuel.yaml"
+
+    === "Technologies"
+
+        **electricity_from_biofuel**: Biofuel-consuming electricity production facility (assuming anaerobic digestion).
+
+??? note "conversion/heat-from-electricity.yaml"
 
     === "Technologies"
 
@@ -221,10 +241,9 @@ Here, we describe each module in terms of the technologies they contain (`callio
 
         **free_transmission**: Local power transmission
 
-
 ## Overrides and scenarios
 
-Calliope [overrides](https://calliope.readthedocs.io/en/v{{ calliope_version }}/user/building.html#scenarios-and-overrides) enable models to be easily manipulated.
+Calliope [overrides](<https://calliope.readthedocs.io/en/v{{> calliope_version }}/user/building.html#scenarios-and-overrides) enable models to be easily manipulated.
 An override named `freeze-hydro-supply-capacities` can be used for example in this way:
 
 ``` bash
@@ -243,7 +262,7 @@ For instance, `freeze-hydro-supply-capacities` and `freeze-hydro-storage-capacit
 You can also define your own overrides to manipulate any model component.
 We recommend you add these overrides into the model definition YAML file, to ensure they are easy to trace.
 
-In Calliope, [scenarios](https://calliope.readthedocs.io/en/v{{ calliope_version }}/user/building.html#scenarios-and-overrides) are groups of overrides and/or other scenarios.
+In Calliope, [scenarios](<https://calliope.readthedocs.io/en/v{{> calliope_version }}/user/building.html#scenarios-and-overrides) are groups of overrides and/or other scenarios.
 In Euro-Calliope, it can be helpful to define scenarios to help group similar overrides together.
 For instance, cost overrides from the Danish Energy Agency are defined in various files, since they are loaded in alongside the technologies they affect (the option to override offshore wind costs only exists when you load the `techs/supply/wind-offshore.yaml` module).
 You can pre-define scenarios in your model definition file, such as:

rules/biofuels.smk

@@ -60,7 +60,6 @@ rule biofuel_tech_module:
         ),
         locations = "build/data/{{resolution}}/biofuel/{scenario}/potential-mwh-per-year.csv".format(scenario=config["parameters"]["jrc-biofuel"]["scenario"])
     params:
-        biofuel_efficiency = config["parameters"]["biofuel-efficiency"],
         scaling_factors = config["scaling-factors"],
     conda: "../envs/default.yaml"
     output: "build/models/{resolution}/techs/supply/biofuel.yaml"

rules/heat.smk

@@ -123,7 +123,7 @@ rule heat_pump_final_timeseries:
     conda: "../envs/default.yaml"
     wildcard_constraints:
         input_dataset = "heat-pump-cop"
-    output: "build/models/{resolution}/timeseries/supply/{input_dataset}.csv"
+    output: "build/models/{resolution}/timeseries/conversion/{input_dataset}.csv"
     script: "../scripts/heat/heat_pump_final_timeseries.py"
 
 

scripts/biofuels/allocate.py

@@ -85,7 +85,7 @@ def biofuel_potential(
             index_col=["year", "scenario", "country_code", "feedstock"],
         )["value"]
         .div(GJ_TO_MWH)
-        .xs((potential_year, scenario), level=("year", "scenario"))
+        .xs((cost_year, scenario), level=("year", "scenario"))
     )
     units = pd.read_csv(path_to_units).set_index("id")
     if (len(units.index) == 1) and (

scripts/biofuels/template_bio.py

@@ -1,35 +1,14 @@
 import pandas as pd
 from eurocalliopelib.template import parametrise_template
 
-
-def construct_techs_and_locations(
-    path_to_template,
-    path_to_output,
-    path_to_locations,
-    path_to_biofuel_costs,
-    biofuel_efficiency,
-    scaling_factors,
-):
-    with open(path_to_biofuel_costs) as f_biofuel_costs:
-        biofuel_fuel_cost = float(f_biofuel_costs.readline())
-    locations = pd.read_csv(path_to_locations, index_col=0)
-
-    return parametrise_template(
-        path_to_template,
-        path_to_output,
-        biofuel_fuel_cost=biofuel_fuel_cost,
-        biofuel_efficiency=biofuel_efficiency,
-        scaling_factors=scaling_factors,
-        locations=locations,
-    )
-
-
 if __name__ == "__main__":
-    construct_techs_and_locations(
-        path_to_template=snakemake.input.template,
-        path_to_locations=snakemake.input.locations,
-        path_to_biofuel_costs=snakemake.input.biofuel_cost,
-        biofuel_efficiency=snakemake.params.biofuel_efficiency,
+    biofuel_cost = float(pd.read_csv(snakemake.input.biofuel_cost).columns[0])
+    locations = pd.read_csv(snakemake.input.locations, index_col=0)
+
+    parametrise_template(
+        snakemake.input.template,
+        snakemake.output[0],
+        biofuel_cost=biofuel_cost,
         scaling_factors=snakemake.params.scaling_factors,
-        path_to_output=snakemake.output[0],
+        locations=locations,
     )

scripts/summarise_potentials.py

@@ -42,7 +42,7 @@ def summarise_potentials(
 
     list_of_techs = model.inputs.techs.values
     list_of_locs = model.inputs.locs.values
-    list_of_potentials = list(considered_potentials.keys())
+    list_of_potentials = list(set(considered_potentials).intersection(model.inputs))
 
     summary = np.empty((len(list_of_techs), len(list_of_potentials), len(list_of_locs)))
     summary[:] = np.nan

templates/models/techs/conversion/electricity-from-biofuel.yaml.jinja

@@ -0,0 +1,19 @@
+techs:
+    electricity_from_biofuel:  # from [@JRC:2014] Table 48 Anaerobic digestion
+        essentials:
+            name: Electricity from anaerobically digested biofuel
+            parent: conversion
+            carrier_in: biofuel
+            carrier_out: electricity
+        constraints:
+            energy_eff: {{ biofuel_efficiency }}
+            lifetime: 20
+        costs.monetary:
+            energy_cap: {{2300000 * scaling_factors.specific_costs}} # {{ (1 / scaling_factors.specific_costs) | unit("EUR2013/MW") }}
+            om_annual: {{2300000 * 0.041 * scaling_factors.specific_costs}} # {{ (1 / scaling_factors.specific_costs) | unit("EUR2013/MW") }} 4.1% of CAPEX
+            om_prod: {{ 3.1 * scaling_factors.specific_costs }} # {{ (1 / scaling_factors.specific_costs) | unit("EUR2013/MWh") }}
+
+locations:
+    {% for id, location in locations.iterrows() %}
+    {{ id }}.techs.electricity_from_biofuel:
+    {% endfor %}

templates/models/techs/conversion/heat-from-biofuel.yaml.jinja

@@ -0,0 +1,29 @@
+{# TODO: weight single-/multi-family technology costs based on regional dwelling ratio - see https://github.com/calliope-project/euro-calliope/issues/406 #}
+{# Costs/efficiency being averaged are given in the order they appear in the spreadsheet (which is the same order as in the inline comments). #}
+{# Costs are given by DEA per technology "unit" (1000EUR/unit), so are converted to a cost per capacity (1000EUR/kW_heating) by dividing by the capacity of one unit, as given in the same data table. #}
+
+techs:
+    biofuel_boiler: # [@DEA:2017] - Biomass boiler, automatic stoking , wood pellets or wood chips - 2050
+        # Costs and efficiency are an average of data for existing single-family, new single-family, existing multi-family, and new multi-family homes.
+        essentials:
+            name: Biofuel boiler
+            parent: conversion
+            carrier_in: biofuel
+            carrier_out: biofuel_heat
+        constraints:
+            energy_eff: {{ mean([0.88, 0.85, 0.90, 0.90]) }}
+            lifetime: 20
+        costs:
+            monetary:
+                energy_cap: {{ mean([5.9 / 10, 5.9 / 8, 76 / 400, 45 / 160]) * 1e6 * scaling_factors.specific_costs }}  # {{ (1 / scaling_factors.specific_costs) | unit("EUR2015/MW_heat") }}
+                om_annual: {{ mean([0.42 / 10, 0.42 / 10, 1.343 / 400, 0.889 / 160]) * 1e6 * scaling_factors.specific_costs }}  # {{ (1 / scaling_factors.specific_costs) | unit("EUR2015/MW_heat/year") }}
+    biofuel_tech_heat_to_demand:
+        essentials.parent: tech_heat_to_demand
+        essentials.carrier_in: biofuel_heat
+
+locations:
+    {% for id, location in locations.iterrows() %}
+    {{ id }}.techs:
+        biofuel_boiler:
+        biofuel_tech_heat_to_demand:
+    {% endfor %}

templates/models/techs/supply/heat-from-electricity.yaml.jinja → templates/models/techs/conversion/heat-from-electricity.yaml.jinja

@@ -2,13 +2,6 @@
 {# Costs being averaged are given in the order they appear in the spreadsheet (which is the same order as in the inline comments). #}
 {# Costs are given by DEA per technology "unit" (1000EUR/unit), so are converted to a cost per capacity (1000EUR/kW_heating) by dividing by the capacity of one unit, as given in the same data table. #}
 
-tech_groups:
-    tech_heat_to_demand:
-        essentials:
-            name: Technology-specific heat carriers to generic heat carrier converter
-            parent: conversion
-            carrier_out: heat
-
 techs:
     heat_pump:   # [@DEA:2017] - 7.3 - 7.6 Air to water & 7.7 - 7.10 Ground source - 2050
         essentials:
@@ -17,7 +10,7 @@ techs:
             carrier_in: electricity
             carrier_out: hp_heat
         constraints:
-            energy_eff: file=supply/heat-pump-cop.csv  # NOTE: based on data processing pipeline, not [@DEA:2017].
+            energy_eff: file=conversion/heat-pump-cop.csv  # NOTE: based on data processing pipeline, not [@DEA:2017].
             lifetime: 20
         costs:
             monetary:

templates/models/techs/demand/heat.yaml.jinja

@@ -1,3 +1,10 @@
+tech_groups:
+    tech_heat_to_demand:
+        essentials:
+            name: Technology-specific heat carriers to generic heat carrier converter
+            parent: conversion
+            carrier_out: heat
+
 techs:
     demand_heat:
         essentials:

templates/models/techs/storage/heat.yaml.jinja

@@ -19,10 +19,14 @@ techs:
     electric_heater_heat_storage_small:
         essentials.parent: heat_storage_small
         essentials.carrier: electric_heater_heat
+    biofuel_heat_storage_small:
+        essentials.parent: heat_storage_small
+        essentials.carrier: biofuel_heat
 
 locations:
     {% for id, location in locations.iterrows() %}
     {{ id }}:
         techs.hp_heat_storage_small:
         techs.electric_heater_heat_storage_small:
+        techs.biofuel_heat_storage_small:
     {% endfor %}

templates/models/techs/supply/biofuel.yaml.jinja

@@ -1,23 +1,34 @@
 techs:
-    biofuel:  # from [@JRC:2014] Table 48 Anaerobic digestion
+    biofuel_supply:
         essentials:
-            name: Biofuel
+            name: Biofuel supply stream
             parent: supply_plus
-            carrier: electricity
+            carrier: biofuel
         constraints:
-            energy_eff: 1.0 # efficiency modelled within the input resource stream to avoid poor numerical scaling
-            lifetime: 20
+            lifetime: 1  # arbritrarily chosen to avoid Calliope errors
         costs.monetary:
-            energy_cap: {{2300000 * scaling_factors.specific_costs}} # {{ (1 / scaling_factors.specific_costs) | unit("EUR2013/MW") }}
-            om_annual: {{2300000 * 0.041 * scaling_factors.specific_costs}} # {{ (1 / scaling_factors.specific_costs) | unit("EUR2013/MW") }} 4.1% of CAPEX
-            om_con: {{ (biofuel_fuel_cost / biofuel_efficiency + 3.1) * scaling_factors.specific_costs }} # {{ (1 / scaling_factors.specific_costs) | unit("EUR2013/MW") }}
-            om_prod: 0 # 3.1 (EUR2013/MWh) added to om_con because value is very small and causing poor numerical range
+            om_prod: {{ biofuel_cost * scaling_factors.specific_costs }} # {{ (1 / scaling_factors.specific_costs) | unit("EUR/MWh") }}
 
 locations:
     {% for id, location in locations.iterrows() %}
-    {{ id }}.techs:
-        biofuel:
-            constraints:
-                resource: {{ location.biofuel_potential_mwh_per_year * biofuel_efficiency / 8760 * scaling_factors.power }} # {{ (1 / scaling_factors.power) | unit("MW") }}
-                storage_cap_equals: {{ location.biofuel_potential_mwh_per_year * biofuel_efficiency / 2 * scaling_factors.power }} # {{ (1 / scaling_factors.power) | unit("MWh") }} (0.5x annual yield) # ASSUME < 1 for numerical range
+    {{ id }}.techs.biofuel_supply:
     {% endfor %}
+
+group_constraints:
+    {% for id, location in locations.iterrows() %}
+    biofuel_max_prod_{{ id }}:
+        locs: [{{ id }}]
+        techs: [biofuel_supply]
+        carrier_prod_max:
+            biofuel: {{ location.biofuel_potential_mwh_per_year * scaling_factors.power }} # {{ (1 / scaling_factors.power) | unit("MWh") }}
+    {% endfor %}
+
+overrides:
+    biofuel_flow_limits:
+        locations:
+            {% for id, location in locations.iterrows() %}
+            {{ id }}.techs.biofuel_supply:
+                constraints:
+                    resource: {{ location.biofuel_potential_mwh_per_year / 8760 * scaling_factors.power }} # {{ (1 / scaling_factors.power) | unit("MW") }}
+                    storage_cap_equals: {{ location.biofuel_potential_mwh_per_year / 2 * scaling_factors.power }} # {{ (1 / scaling_factors.power) | unit("MWh") }} (0.5x annual yield) # ASSUME < 1 for numerical range
+            {% endfor %}